We aim to provide a general framework of for computational photography that recovers the real scene from imperfect images, via the Deep Nonparametric Convexified Filtering (DNCF). It is consists of a nonparametric deep network to resemble the physical equations behind the image formation, such as denoising, super-resolution, inpainting, and flash. DNCF has no parameterization dependent on training data, therefore has a strong generalization and robustness to adversarial image manipulation. During inference, we also encourage the network parameters to be nonnegative and create a bi-convex function on the input and parameters, and this adapts to second-order optimization algorithms with insufficient running time, having 10X acceleration over Deep Image Prior. With these tools, we empirically verify its capability to defend image classification deep networks against adversary attack algorithms in real-time.

From X-rays to magnetic resonance imaging (MRI), methods for scanning the body have transformed how we understand and care for our health. These non-invasive techniques allow clinicians to observe and diagnose conditions while minimizing risks to the patient from invasive medical procedures. Recently, methods using red-green-blue (RGB) and near-infrared (NIR) cameras, other photosensors, such as more specialized and sophisticated tomography,6,8,40 and radio waves and Wi-Fi signals have enabled a range of new non-invasive and non-contact health monitoring techniques.1,8,12,21,22,26,28,32,33,40 Human tissue interaction with visible and infra-red light is predominantly through scattering. There are two immediate consequences to this light scattering by tissue.

Last weekend The New Yorker published an essay by Kyle Chayka with a headline guaranteed to pique my interest and raise my hackles: "Have iPhone Cameras Become Too Smart?" (March 18, 2022). Aside from being a prime example of Betteridge's Law of Headlines, it feeds into the idea that computational photography is a threat to photographers or is somehow ruining photography. The subhead renders the verdict in the way that eye-catching headlines do: "Apple's newest smartphone models use machine learning to make every image look professionally taken. That doesn't mean the photos are good." The implication there, and a thrust of the article, is that machine learning is creating bad images.

For spontaneous photography, the best camera for the job is the one you happen to be holding. For the overwhelming majority of casual photographers, that camera is the one in your smartphone. The very first cameras appeared on commercial mobile phones around the turn of the century (although as ever with tech milestones, there are multiple claims to the title of pioneer). The early work of companies like Kyocera, Motorola, Samsung, and even Apple, opened the floodgates to what is now best called'computational photography'; relatively small sensors and lenses, paired with massively sophisticated algorithms and processing to deliver the kind of images that even the best DSLR cameras would struggle to match. One company believes that mobile photography could be better.

There are now two ways of creating digital images with a camera. You can either follow a software-centric computational photography approach. The other way is to stick to traditional hardware-centric optical photography. The former is used with AI to help enhance the final image, the latter relies on the quality of the camera's components (e.g. The two techniques may differ, but they are not at all on a collision course.

Artificial intelligence is leading to some drastic changes in the field of photography. Many photographers are discovering the profound benefits of machine learning and other AI capabilities. The market for artificial intelligence in photography was worth $10.7 billion in 2019. It is expected to reach over $29 billion by 2024. There have already been a lot of applications for machine learning with photos in marketing.

How GPT-3, your smartphone and Augmented Reality can disrupt a dinosaur industry. The earliest photographic studios made use of painters' lighting techniques to create portraits. In my country, generations of Indians would assemble under the studio lights to get that perfect family portrait. We have come a staggering distance since then. Today, these photo studios that were responsible for many families and their portraits, have all but disappeared.

I had a lot of fun answering. If you're interested in me or the app, please follow us on twitter or insta (@genevievemp and @thetrashapp). If you sent me messages or emails, I'll get back to you as soon as I can. My name is Genevieve Patterson - I'm the Chief Scientist at TRASH, and a PhD in Computer Vision. I've been working on our AI, Otto, for over a year now, and it's getting smarter with every release - here is a blog post about our latest version, and how it collaborates with user inputs.

Visual 1st, the executive conference focused on promoting innovation and partnerships in the photo and video ecosystem, will bring AI, AR, computational photography, and the future of digital cameras to the center stage, Oct. 3-4, at the Golden Gate Club, San Francisco, Calif. AI is already everywhere in imaging, from recognition to enhancement to auto-editing – and of course, there's much more to come. In parallel, AR solutions are proliferating at a rapid pace, serving use cases ranging from having lots of fun to being highly productive. As these two technologies evolve in mutually reinforcing ways, we, as an industry, must take the imaging solutions they enable to the next level of value and profitability, while also keeping things safe, secure and private for our customers – but how? Alexander Schiffhauer recently left his role as Technical Advisor to Google's CEO Sundar Pichai to take product management responsibility for the company's computational photography teams. Under his leadership, these teams have pioneered innovation on Pixel Camera, leveraging AI and computer vision techniques to create photos unimaginable only a few years ago.

Since their introduction more than a decade ago, smartphones have been equipped with cameras, allowing users to capture images and video without carrying a separate device. Thanks to the use of computational photographic technologies, which utilize algorithms to adjust photographic parameters in order to optimize them for specific situations, users with little or no photographic training can often achieve excellent results. The boundaries of what constitutes computational photography are not clearly defined, though there is some agreement that the term refers to the use of hardware such as lenses and image sensors to capture image data, and then applying software algorithms to automatically adjust the image parameters to yield an image. Examples of computational photography technology can be found in most recent smartphones and some standalone cameras, including high dynamic range imaging (HDR), auto-focus (AF), image stabilization, shot bracketing, and the ability to deploy various filters, among many other features. These features allow amateur photographers to produce pictures that can, at times, rival photographs taken by professionals using significantly more expensive equipment.